Deep Residual CNN Based Model for Human Activity Recognition System

Abstract

Human Action Recognition (HAR) is a significant application realm in computer vision, but high precision recognition of human action in the complex background is still an open question. Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In our research, as classifier, a deep Convolutional Neural Network (CNN) using ResNet-50 model is proposed for HAR because it is the most upper hand in compare to other classifiers. Our proposed research work have used publicly accessible UCF-101 dataset which provides the largest multiplicity in HAR filed as most of the available action recognition data sets are not realistic. Additionally, UCF-101 dataset intends to give support further research into action recognition by learning and surveying new pragmatic action categories.